Radio Frequency Identification Tag Location Estimation and Tracking System and Method

ABSTRACT

Systems and methods for locating one or more radio frequency identification (RFID) tags are provided. A phase difference of received information signals of illuminated RFID tags is utilized to locate the RFID tags. One or more exciters transmit interrogation signals to illuminate the RFID tags in which the exciters may have a plurality of antenna selectively configured to transmit through two or more antennas and to receive on one antenna. Multiple reads of the same RFID tag can also be performed to generate a probability model of the location of the RFID tag. An enhanced particle filter is applied to probability model to determine the exact location of the RFID.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.14/136,653, filed Dec. 20, 2013, which application is a continuation ofU.S. application Ser. No. 13/309,329, filed Dec. 1, 2011, whichapplication is a continuation of U.S. application Ser. No. 12/423,796filed Apr. 14, 2009, which application claims the benefit of U.S.Provisional Patent Application No. 61/124,294, filed Apr. 14, 2008 andU.S. Provisional Patent Application No. 61/044,904, filed Apr. 14, 2008,the disclosures of which are hereby incorporated by reference as if setforth in full herein.

BACKGROUND

This application relates to estimating location and tracking of passiveor active sensors and in particular is related to using phased arrayantenna systems and Radio Frequency Identification (RFID) system tolocate sensors and/or RFID tags.

An RFID system conventionally includes a set of stationary or mobileRFID tags typically manipulated by a reader/interrogator system. Eachsensor may be passive or active, i.e., with or without a battery. Inconventional systems, the reader and the RFID tags are generallyrequired to be in close proximity so that the tags can operate in closeproximity to the reader antenna.

The limited transmission distances available with conventional RFIDsystems limit their use in an automated factory setting and/or in anindoor wireless environment. Even within the designed range ofoperation, such systems often have low reliability due to interferencesand collisions.

Typical RFID systems also are not designed to cover extremely largeareas as multiple base stations are needed to provide sufficientcoverage for the area. This can be extremely expensive and thus costprohibitive. Also, sacrifices are made to compensate for the large areaby limiting the coverage area to select high use regions, e.g., at dockdoors. Additionally, such systems often do not provide precise locationdeterminations of the RFID due to the complexity in the size and spaceof the environment. Accordingly, there is a need for a RFID system thatovercomes the above-noted obstacles and the shorting comings in the art.

SUMMARY

In one aspect, the location of RFID tags/sensors is determined usingboth single and multiple read points.

In one embodiment, a method of locating one or more radio frequencyidentification (RFID) tags comprises illuminating at least one RFID tagby an exciter; receiving information signals from the illuminated atleast one RFID tag by a plurality of receive antennas; determining aphase derivate for the received information signals from the at leastone illuminated RFID tag received by each of the plurality of receiveantennas; and identifying a location of the at least one RFID tag basedon the determined phase derivates of the received information signals.Also, in one embodiment, the method further includes identifying thelocation of the at least one RFID based on a ratio of the phase derivateversus the frequency derivate.

In another embodiment, a radio frequency identification (RFID) systemfor locating one or more RFIDs comprises at least one exciter and areader. The at least one exciter has a plurality of antennas and isconfigured to selectively transmit interrogation signals through atleast two of the plurality of antennas and to selectively receiveinformation signals from at least one RFID tag through one of theplurality of antennas different from the at least two of the pluralityof antennas. The reader is in communication with the at least oneexciter and is configured to activate the at least one exciter. Thereader locates the at least one RFID tag based on a phase derivate ofthe received information signals.

In yet another embodiment, a radio frequency identification (RFID)system for locating one or more RFIDs comprises at least one RFID tag,an antenna array, a transmitter and a reader. The antenna array isconfigured to illuminate the at least one RFID tag. The transmitter iscoupled to the antenna array and is configured to activate the antennaarray to repeatedly illuminate the at least one RFID tag within aspecific time frame and a specific space. The reader is in communicationwith the transmitter and is configured to generate a probability modelbased on information signals received from the repeated illumination ofthe at least one RFID tag and the reader applies a particle filter onthe generated probability model to determine a location of the at leastone RFID tag based on a result of the applied particle filter.

In a further embodiment, a method of locating one or more radiofrequency identification (RFID) tags comprises positioning at least onereceiver and at least one transmitter to share a geometriccharacteristic with each other; determining location measurements basedon received information signals from at least one RFID tag illuminatedby the at least one transmitter; and estimating a location of the atleast one RFID tag utilizing a probability model and the determinedlocation measurements.

For a more complete understanding of the disclosed method and system,reference is now made to the following description taken in conjunctionwith the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a distributed exciter architectureshowing TX and receive coverage areas for two readers as well as exciterinterrogation spaces in accordance with various aspects of the presentinvention.

FIG. 2 is a schematic diagram of a distributed exciter architectureshowing TX and receive coverage areas for two readers as well as exciterinterrogation spaces in accordance with various aspects of the presentinvention.

FIG. 3 is a distributed exciter architecture cabled (single) systemshowing TX and receive coverage areas for the reader as well as exciterinterrogation spaces in accordance with various aspects of the presentinvention.

FIGS. 4A-B illustrate an exciter layout and an exemplary frequency planin accordance with various aspects of the present invention.

FIG. 5 graphically illustrates an algorithm for estimation of the angleof arrival from the received signal in accordance with various aspectsof the present invention.

FIG. 6 is a block diagram of a reader system showing RFID Tags,Interferer sources, and the reader (i.e., transmit, and receive chain)in accordance with various aspects of the present invention.

FIG. 7 is a functional block diagram of the reader showing an Antennaarray, RF/IF, signal processing and synthesizer subsystem in accordancewith various aspects of the present invention.

FIG. 8 is a graphical representation illustrating a reader array and asingle excitation point topology in accordance with various embodimentof the present invention.

FIGS. 9A-9C are graphical representations providing ellipticalrepresentations for determining the location an RFID tag in accordancewith various embodiments of the invention.

FIG. 10 is a block diagram of a four port excitation node in accordancewith various embodiments of the invention.

FIG. 11 is a detailed block diagram of a four port excitation node inaccordance with various embodiments of the invention.

FIG. 12 is a graphical representation illustrating a distributed arrayof readers and a single excitation point topology in accordance withvarious embodiments of the invention.

FIG. 13A-D is a graphical representation illustrating a four portexcitation node in a chandelier configuration in accordance with variousembodiments of the invention.

FIG. 14A-D is a graphical representation illustrating a four portexcitation node in an offset linear array configuration in accordancewith various embodiments of the invention.

FIG. 15A-F is a graphical representation illustrating a four portexcitation node in an offset linear array configuration relative to sixdifferent RFID tag locations in accordance with various embodiments ofthe invention.

FIG. 16 is a graphical representation illustrating a generalizedmulti-port excitation node, or array of nodes, in an arbitrary orirregular configuration in accordance with various embodiments of theinvention.

FIG. 17 is a flow chart of a particle filter location procedure forlocating a RFID tag in accordance with various embodiments of theinvention.

FIGS. 18-19 are flow diagrams providing an overview of locationestimation performed by a reader in accordance with various aspects ofthe present invention.

FIG. 20 graphically illustrates a simplified four-element array in a 2Dimplementation in accordance with various aspects of the presentinvention.

FIG. 21 graphically illustrates an analysis setup for DOA analysisshowing RFID Tag, and antenna in accordance with various aspects of thepresent invention.

FIG. 22 is an interrogation space viewed as a two dimensional Gaussiandensity with a known mean and variance in two dimensional (x,y)Euclidean space in accordance with various aspects of the presentinvention.

FIG. 23 illustrates a Markov chain assumption in accordance with variousaspects of the present invention.

FIG. 24 is a flow diagram illustrating steps of the general form ofsequential Monte Carlo methods in accordance with various aspects of thepresent invention.

FIG. 25 is a flow diagram illustrating steps for the general solution tofinding the location of the RFID tag/sensor in accordance with variousaspects of the present invention.

FIG. 26 is a flowchart of a Differential and Genetic evolutionmethodology in accordance with various aspects of the present invention.

FIG. 27 is a conceptual illustration showing application of autonomousperpetual inventory to items stored on vertically racked shelves.

DETAILED DESCRIPTION

Referring now to the drawings, systems and methods for locating one ormore radio frequency identification (RFID) tags are described. Thesystems utilize various transmitter and receiver geometries to obtainobservations relevant to the location of RFID tags. The observations canbe provided to any of a variety of estimators that can generateestimates for the location of the observed RFID tags.

The transmitter and receiver geometry of an RFID system influences theaccuracy with which the system can estimate the location of RFID tags.Various architectures in accordance with embodiments of the inventionare discussed. In a number of embodiments, one or more exciters transmitinterrogation signals to illuminate the RFID tags and the reflectedsignals are received by a plurality of receivers. The receivers can beseparate receivers and/or can be separate receiver antennas connected toa single receiver system or a multiple port exciter system. In severalembodiments, a multiple port exciter system acts as both the exciter andthe receiver. One of the antennas is selectively configured to transmitinterrogation signals and the remaining antennas are configured toreceive signals backscattered by RFID tags. In a number of embodiments,the multiport exciters do not possess the ability to read data from RFIDtags and simply possess the ability to make observations useful inlocating the RFID tags.

Due to instability in the RFID backscatter process, the observables thatare chosen when performing location estimation can influence theaccuracy of the resulting estimates. In various embodiments, the systemobserves the phase difference of backscattered signals from illuminatedRFID tags. In several embodiments, the phase differences are observed atdifferent transmit frequencies to provide range information. The ratioof phase difference to frequency difference is also referred to as groupdelay. In many embodiments, the system observes the read rate of RFIDtags in response to illumination of different interrogation spaces byvarious exciters. The read rate is the number of times that a tag isread as a ratio of the number of opportunities that the tag had to beread. In systems that utilize extremely sensitive receivers, read ratecan be considered indicative of the distance from the RFID tag to theexciter. In such systems, the overwhelming majority of tags that areactivated by an exciter are read. Therefore, read rate is largelyindicative of the rate of activation of the RFID tag by the transmitter.

As is discussed further below, a variety of techniques can be used toestimate location based upon one or more observables in accordanceaspects of the invention. Given the complexity of the system and thenumerous RFID tags potentially in a given space, statistical modeling ofthe RFID tag locations can provide accurate location estimates for eachRFID tag within the space. As such, in various embodiments, by usingmany observations of the RFID tags, a probability distribution model iscreated. Using one or more algorithms and/or filters, the model isfurther refined to determine the location to the RFID tag. In severalembodiments, a particle filter is utilized to create and refine theprobability distribution model. In other embodiments, a variety of othertechniques can be used to refine the location estimates obtained usingthe observables.

System Architectures

The ability to locate RFID tags within a given space is largelydependent upon the location of the antennas used to transmitinterrogation signals to the RFID tags and the antennas used to receivesignals backscattered by the RFID tags. A variety of geometries can beused in accordance with embodiments of the invention includinggeometries in which the transmit and receive functions are decoupled andcan be performed by separate exciters and receivers.

In a co-pending patent U.S. patent application Ser. No. 12/054,331,filed Mar. 23, 2007, entitled “RFID Systems Using Distributed ExciterNetwork”, the disclosure of which incorporated by reference as if setforth in full herein, enhancement in the performance and capacity forRFID systems is achieved by separating the receive and transmit systemsto manipulate the passive RFID tags. This functionality is realized bydecomposing the population of RFID tags/sensors into a set ofinterrogation spaces (1-16, 1-32, 1-38, 1-44, 1-40, 1-28, 1-24, 1-48,1-54, 1-56) where an exciter is placed for each target interrogationspace (1-18, 1-34, 1-36, 1-46, 1-42, 1-30, 1-26, 1-58, 1-52, 1-50) asshown in FIGS. 1-2.

The size of each interrogation space can be adjusted by controlling thetotal emitted power from the exciter. However, it should be appreciatedthat emitted power of an RFID system is typically restricted byregulation and limits the interrogation range of an exciter, e.g., 20 to30 feet. Emitter power control is implemented through the RFID reader's(1-2) exciter power management and gain controller subsystem (3-18,3-30). In addition to adjusting the size of each interrogation space,the overall performance of the system may be further improved byselecting each exciter transmit antenna type to provide a desired levelof directivity, thereby controlling the beam-width for the targetinterrogation space.

In a number of embodiments, the reader includes phased-array antennathat is capable of performing beam forming. The reader receivephased-array antenna beam (1-4) can be formed to focus (1-17, 1-21) tospecified interrogation spaces or as a wide beam (1-20). The network oftransmit antennas, also referred to as distributed exciters may becommanded by wired (2-24,2-30, 2-36, 2-12,2-56) or wireless links. Thetransmitted “backhaul signal” from the controller to the exciter embedsall the necessary signal characteristics and parameters to generate thedesired waveform output from the exciter module to the tag. FIGS. 1 and2 also show the RFID application management and operations server (1-7,2-51), which is connected to the reader via the local area network(1-11, 2-50).

FIG. 3 illustrates the layout of a distributed exciter/transmitter RFIDsystems, showing the receive system (3-2), receive antenna array (3-4),and distributed exciters (3-6, 3-14, 3-18, 3-24 & 3-30), which in oneaspect are connected to the system via coaxial cables (3-10, 3-9, 3-16,3-22, & 3-26). The interrogation space and transmitted power of eachexciter is managed and controlled by the central unit (3-2). In FIG. 3,exciter interrogation spaces (3-8, 3-15, 3-20, 3-23, & 3-28) withdifferent sizes are depicted. The complete receive array coverage area(3-11) is also shown. Each interrogation space (3-8, 3-14, 3-20, 3-22,3-28) can be operated sequentially or concurrently, depending on thenumber of possible beams the receiving array supports.

The controller (3-30) in the system (3-2) schedules each exciter tooperate in the time, frequency and space dimensions. The scheduler forS/T/FDM (Space, Time and Frequency Division Multiplexing) utilizes anoptimization algorithm to maximize the probability of reading all thetags within a target interrogation space. The controller may utilizefrequency hopping (while satisfying regulatory requirements) to schedulefrequency channel use for each exciter. FIGS. 4A-B illustrate an exampleof the exciter layout (4-6) and the time line (4-4) showing the assignedfrequency channels (4-8). The timeline (4-4) depicts frequency-hoppedchannelization in the 900 MHz ISM band (4-10). On each timeline, adifferent hopping sequence effectively assigns a distinct series ofrandom frequencies to each active exciter (4-8). The algorithmsdescribed in FIG. 14, manage and optimize this activity. As differentexciters are activated in accordance with the schedule, the RFID systemcan collect observations concerning the RFID tags. In many embodiments,the schedule provides an important role in preventing interference frommultiple exciters during the collection of observations. As is discussedfurther below, the ability to utilize different excitation frequenciesin the schedule enables the RFID system to collect observationsconcerning the group delay of different RFID tags.

Referring back to FIG. 1, a wireless exciter layout and deploymentsystem is shown. In the illustrated embodiment, two RFID systems (1-2,1-14) are provided. Each system has separate receive (1-4, 1-12) andtransmit antennas (1-6, 1-13). The transmit antenna radiates a forwardlink to the exciter, while the receive antenna array receives the signalfrom the tags, within the interrogation space of each exciter. Theforward link in one aspect carries additional information such as anexciter identification (ID) number, command, control and managementinformation. The figure shows the receive coverage areas for two systems(1-22, 1-20). As noted in the figure, the system supports receiveinterrogation spaces (1-24, 1-48, 1-28, 1-40, 1-54, 1-56, 1-44, 1-16,1-32, 1-38). The overlap region between the receive coverage areas(1-22, 1-20) is managed through receive array beam-forming and frequencyor time coordination of exciter operations. The two systems areconnected to the LAN (1-3, 1-8, 1-9) in a way that is similar to thewired exciter systems of FIG. 2, the wireless exciter management server(1-7) interfaces with the two systems (1-2, 1-14) through the LAN andmanages the operations of the exciters that includes control, command,coordination, and calibration of the exciters as well as optimization ofthe interrogation spaces.

Interrogation of RFID Tags

A sensor or an RFID tag can be interrogated many times over a fixed timeinterval. For each of these interrogations, sensor data or informationembedded in RFID tags may be detected by combining the impinging signalon the array to form a single (beamformed) signal for detecting thesequence of symbols transmitted by the RFID tag. Each interrogationround is comprised typically of multiple (e.g. two, referred to as RN16and EPC packet) packets from a cingulated RFID tag. The payload in thesepackets typically contain a temporary address (e.g. a random 16-bitnumber in an RN16 type packet), once acknowledged, the tag may thentransmit a packet with its information content (e.g. an ElectronicProduct Code (EPC)).

Multiple RFID tag interrogation signals can be transmitted at differentfrequencies during each interrogation round. Interrogating a tag usingdifferent frequencies enables additional observation of the tag toaccurately model the received signal phase and amplitude trajectoriesover time, and characterize the signal dispersion with multipathreflections of the transmitted signal.

Overview of Use of Interrogation Results in Location Estimation

During an interrogation round, processing operations may include but arenot limited to: estimating the relative phase difference between thesignals from each of the antenna elements and the reference signal andderiving the relative range from each of the antenna elements to theRFID tag in accordance with the adjusted phase delay difference for eachsuch antenna element. Estimating the location of the RFID tag may thenbe dealt with by treating the aggregate interrogation rounds as a singledata base forming a “sample space”. It is also noted that reading thesame RFID tag at multiple frequencies enables an estimation of the range(distance of the tag to the read point) of the signal source via“sequential ranging”. For applications where only a single reader (readpoint) is deployed, the reader system is able to provide locationestimation without the need to “triangulate”.

Processing the signals may include deriving the adjusted phasedifference between the signals from each of the other antenna elementsand the reference signal, and deriving relative direction of arrival ofthe received signals from the RFID tag at each of the antenna elements.The direction of arrival of a signal from a single RFID tag at multipleread points may be used to further improve the estimate of the locationof the RFID tag. This can be performed by combining the relativedirection of arrival information derived from signals received at eacharray element at each read point. By using multiple interrogation cyclesand arrays, each multiplicity of the number of read points in timeresults in a further enhancement of the overall estimate of the locationof the tag.

The iterating procedure with multiple read points may include combiningthe RFID tag information derived from a plurality of iterations to forma probability distribution of the location of the tag, and applying analgorithm to estimate and mitigate the effects of the multipath in thedirection of arrival of the signal from the source to each antennaelement.

An algorithm for estimation of the angle of arrival from the receivedsignal in a system described in FIG. 5 is addressed in co-pending U.S.patent application Ser. No. 11/770,712, filed Jun. 28, 2007, thedisclosure of which is incorporated by reference above. In theapplication, the method by which the discrete time sampled signal fromeach received packet from the tag is used to estimate the AOA, using therelative phase and amplitudes of the received signal from each antennaelement, is described. The algorithm described in FIG. 5, takes an FFT(5-14) of the array input (5-16), followed by cross spectral matrixcomputation (5-12), decomposition process (5-10), and computation of thebeam steering response (5-8). The steering response computation (5-8)uses the steering vector data (5-6), which is generated and controlledusing array response and calibration data base (5-2, 5-7). The beamsteering response computation (5-8) is repeated for different directionsby changing the cross spectral matrix (5-12).

To handle practical challenges induced by electronic components used inthe radio frequency circuit of the antenna array, among otherchallenges, each antenna element is periodically calibrated in order toeliminate relative phase and amplitude imbalances for each antennaelement and its respective in-phase and quadrature components. Thecalibration is performed for one or more test signals and the processingof the signals received by each antenna element may be corrected tocompensate for such imbalances.

In one embodiment, where an exciter location is not known or to ensurethe exciter's location has not moved, a calibration sequence occursprior to or at various times in a location estimation procedure todetermine the exciter or a dummy RFID tag location. In one embodiment,the exciter or dummy RFID tag location is determined by the RFIDreceiver system similar to the location estimation of the “actual” RFIDtag.

A radio frequency identification system reader in one embodiment isprovided employing an antenna array. In the forward channel (thetransmission path between the reader and the tag), the transmit antennaarray may be distributed across several physical arrays. In the case ofa distributed transmit antenna, the receive antenna array can capturethe impinging energy from the tag signal excited by the antenna elementsof a distributed array. This approach may use spatial multiplexing toprovide substantial bandwidth utilization enhancements over singleantenna systems. The antenna array may support multiple frequency bands.A typical array element configuration includes an aperture-coupled feedtiled patch antenna. The tiled construction includes a matrix ofidentical elements in a two-dimensional plane. A low-noise amplifier(LNA) may be embedded in the antenna element itself to enhance theoverall performance of the system.

For cases in which a transmit array antenna is used, beam forming can beemployed to steer the transmitted beam to a desired location in space.This beam steering reduces the collisions and interference between thesignals received from responding tags. Various transmission policies maybe adopted, as an example: the transmit beamformer coefficients may beupdated every time-slot to inject a “space hopping” pattern to maximizethe received isotropic power to the RFID tags, while satisfyingregulatory constraints for the maximum amount of power and dwell time.

Through periodical calibrations, the beam former may compensate formismatches and imperfections of RF microwave devices in the front end(between the antenna and analog-digital converters (ADCs) for thereceive path and between the digital-analog converters (DACs) and theantenna for the transmit path) as well as mismatches in phase andamplitude from RF-to-baseband from multiple independent parallel arrayelement paths.

Referring now to FIG. 6, a RFID reader interrogating a group of RFIDtags placed on a number of inventory items as arranged on a pallet inaccordance with an embodiment of the invention is shown. The RFID systemoperates in the presence of interference from an exemplary interferer6-10. The pallet of goods 6-1 includes many cases or items tagged withRFID passive tags. A transmitted interrogation signal 6-4 from anantenna 6-6 impinges upon the pallet 6-1. In response to the signalenergy detected by each tag, each tag may backscatter a sequence ofinformation using the power received from the transmitted interrogationsignal or beam 6-4. In the environment, there may be man-made or naturalinterferences illustrated as an interferer 6-10. The receive antennaarray 6-12 applies beam forming to the backscatter signal from the tagso that the received power from the tag is maximized and the powerreceived by antenna 6-6 from the interferer 6-10 is minimized.

In FIG. 7, a functional block diagram of a reader system (e.g., FIG. 6)including an antenna array subsystem (17-1), an RF/IF subsystem (17-2),and a signal processing subsystem (17-3) is shown. Also, a synthesizersubsystem (17-4) provides the clocks and local frequencies to RF/IFsubsystem of the reader system. The reader system interrogates the RFIDtags on the pallet 1-1 (e.g., FIG. 1) in the presence of interference.

Antenna Geometries

RFID systems can include multiple transmit antennas and multiple receiveantennas. In a distributed exciter architecture, the multiple transmitantennas are the antennas of exciters. As will be discussed below, themultiple receive antennas can be the antenna array of an RFID receiverand/or the antennas of a multiport exciter switched to make observationsfor the purpose of location estimation. When collecting observations forthe purpose of estimating RFID tag location, the number and position ofthe receive antennas relative to the exciter and relative to each othercan materially impact the accuracy with which any individual observationcan be made. Therefore, the geometry of the transmitter and receiverantennas can impact the number of antennas required for an applicationwith a specified location estimation precision requirement.

Linear Arrays

Referring now to FIGS. 8-9C, a reader array 18-1 relative to a singleexciter 18-3 and RFID tag 18-2 is shown. The reader array is a lineararray including four antennas providing four reception points. Eachantenna is offset relative to each other. The exciter is positioned aspecific distance d1 from the reader array and also a specific distanced2 relative to a RFID tag. The RFID tag is also located a specificdistance d3 relative to the reader array.

Observations Using Receiver Arrays

In FIGS. 9A-9C, observation of the location of an RFID tag utilizingcalibrated slope of the group delay as a proxy for distance from theRFID receiver system to the exciter (d1) from the exciter to the RFIDtag (d2) and from the RFID tag back to the RFID receiver system (d3) inaccordance with an embodiment of the invention is shown. Group delayobservations are discussed in greater detail below, but can be assumedhere as providing an estimate of the path length between a transmitantenna, an RFID tag and a receive antenna. An ellipse is a locus ofpoints in a plane such that the sum of the distances to two fixed pointsis a constant. Such is exactly the case when the distance between areader and exciter is known (d1) and the sum of the distances d2+d3 isknown. By way of calibration we are able to determine the time(distance) between the reader (noted by a star) and an exciter (noted bya diamond). We are also able to determine the sum of times d1+d2+d3.These two distance measurements together (d1 and d1+d2+d3) imply thatthe tag must be somewhere on the ellipse 18-4. Note that there is adegenerate case wherein 2*d1=d1+d2+d3. This indicates that the tag issomewhere on the line segment between the reader and the exciter;exactly where cannot be determined in this case (an additional exciter,as will be described, resolves this instance). Expressions fordetermining the location of the intersection of a line emanating fromone foci of an ellipse (in this case the reader) and a position on theellipse can be derived.

In FIG. 9B, the case in which a tag is on the line segment between theReader and Exciter 1 is provided. This is the degenerate case wherein2d1=d1+d2+d3. In this case the region of possible tag location accordingto Exciter 1 is the line segment between the Reader and Exciter 1. Ifanother exciter, Exciter 2 (18-4), is able to illuminate the tag, thenthis case can be resolved be finding the intersection between theellipse predicted (18-5) by the foci locations of the Reader and Exciter2 and the sum of the distances d22, distance from exciter 2 and the tagand distance d32, distance from the reader to the exciter 2. It shouldbe appreciated that in this case the angle of arrival (AOA) informationis not required to determine tag location. It can, however, be used asan additional measurement via the procedures previously described.

In FIG. 9C, two non-degenerate location predictions due to Exciter 1 andExciter 2 are shown. Both distances d11 and d12, distances from thereader to each respective exciter, can be determined via a calibrationprocedure. Distances d21+d31 and d22+d32 specify left and right ellipserespectively. Angles φ₁ and φ₂ predict the line segment leaving theReader and the intersection of this segment with each of the ellipsesspecifies the tag location. The angles φ₁ and φ₂ as shown appear thesame. However, each angle is an independent observation due to readingthe tag of interest via excitation from exciters 1 and 2 respectively.

Multiport Exciter Geometries

Distributed exciter architectures decouple the transmit and receivefunctions in an RFID system, with exciters being tasked with performingthe transmit function. An advantage of decoupling the system in this wayis that low cost exciters can be used an deployed in more locations thantraditional RFID receiver systems, where providing RFID receivers inmultiple locations is typically too costly. In a number of embodiments,exciters have multiple ports so that a single exciter can activate RFIDtags using multiple antennas (i.e. ports). In many embodiments, themultiport exciters can possess the capability to switch some antennas toreceive signals backscattered by RFID tags. Switching the function ofthe antennas in this way enables the multiport exciter to collectobservations of the signals backscattered by the RFID tags. The exciterscan make these observations without the necessity of the complexdecoding circuitry utilized in RFID receivers. Enabling multiportexciters to collect observations concerning RFID tags significantlyincreases the number of receive antennas within an RFID system that canbe utilized to collect information for use in location estimation. Inaddition, the antennas of a multiport exciter are typically distributedfurther from each other than the antennas in the linear array of an RFIDreceiver. Multiport exciters that can collect observations concerningRFID tags and various geometries for the location of the antennas of amultiport exciter are discussed further below.

Turning now to FIG. 10, FIG. 10 illustrates a block diagram of amultiport exciter or excitation node (eNode) having four ports (fourport exciter) in accordance with various embodiments of the invention.Although much of the following discussion relates to a multiport exciterincluding four ports, multiport exciters including any number of portscan be utilized in accordance with embodiments of the invention. Thefour port exciter includes switch circuitry 19-1 to select one or moreantennas 19-2. The selection of the antennas is controlled by aprocessor 19-3 utilizing an associated transmitter circuitry 19-4 orreceiver circuitry 19-5. The transmitter circuitry handles all outgoingcommunications such as interrogation and calibration signals to nearbyRFIDs. The receiver circuitry handles all incoming communications suchas response data from interrogated RFIDs. The receiver circuitry throughthe switch circuitry selects one of the antennas for reception ofincoming signals. Likewise, the transmitter circuitry through the switchcircuitry selects one of the antennas for transmission of the outgoingsignals. In most cases, multiple antennas are selected for thetransmission to ensure the maximum interrogation coverage to reach themost RFIDs nearby and as described throughout the application to quicklyand accurately determine the location of the RFIDs.

FIG. 11 illustrates a detailed block diagram of a multiport exciter orexcitation node (eNode) in accordance with various embodiments of theinvention. The excitation node includes an antenna array that transmitsand receives data from RFIDs. The antenna array includes multipleantenna elements 7-1 through 7-4. Each antenna element is configured viaa switch (7-5), such that one or more of the antenna elements transmitsand the remaining antenna elements receive. As such, a received signalvia the antenna element 7-1 is supplied to an amplifier 7-10. Thereceive path signal is thus amplified (7-10), bandpass filtered (7-14),and mixed directly to a baseband signal (7-20) supplied by a localoscillator (7-60). The carrier frequency used to mix the baseband signalwith the received signal corresponds in frequency used to mix to RF onthe transmit side (7-62). The received baseband signal is then low passfiltered (7-24) and amplified (7-28). The signal is sampled, correlatedto extract phase, demodulated and decoded by a receiver signalprocessing circuitry (7-70). Command and control messages (non RFID-tagrelated data) are decoded by control circuitry (7-74). Based on thedecoded command and control messages, processor (7-80) issuesinstructions to control transmit power level calibration (7-86) andother maintenance features. Received RFID data is forwarded to a dataencoder and modulator (7-31) to satisfy aspects of different RFIDprotocols. Packets for transmission are upconverted (7-32), bandpassfiltered (7-36), variably attenuated (7-42), and amplified (7-44). Afinal bandpass filter ensures that out of band emissions requirementsare applied (7-52) prior to radiation out through one of a plurality (inthis case four) of possible antennas. In one embodiment, a coaxial cablesupplies a frequency reference, DC power, and command control. In thisembodiment, local oscillator block (7-62) is replaced with a filter thatspectrally purifies the coaxial fed frequency reference.

Referring now to FIG. 12, the antennas of a multiport exciter are shownconfigured as a distributed array of phase synchronized readers at readpoints 20-1, 20-2 and 20-3 and a single exciter 20-5. The location of anRFID tag 20-4 within the distributed array is also shown. The multiportexciter is configured to receive signals backscattered by the RFID tagon a plurality of ports via the antennas located at the read points20-1, 20-2, and 20-3 and to transmit interrogation signals using asingle port via the exciter antenna 20-5. Each read point is a knowndistance from the excitation node. For example, read points 1, 2 and 3are spaced a specific distance d11, d21 and d31, respectively relativeto the excitation node. Also, respective distances d13, d23 and d33relative to each read points 1, 2 and 3 to the RFID tag are determinablealong with the distance d22 relative to the excitation point to the RFIDtag (see discussion of group delays below). Accordingly, utilizing thesums of distances d12+d13, d22+d23 and d32+d33 in conjunction with apriori known read point and excitation point locations to determineellipses, the multiport exciter an collect observations concerning theRFID tag location. One would appreciate that no angle of arrivalinformation is utilized or required to locate the RFID tag.

Turning now to FIGS. 13-16, exemplary excitation nodes or eNodeconfigurations are shown. For example, in FIGS. 13A-D, observations ofan RFID tag obtained by a ‘4-port’ eNode configured as a “chandelier”and the different ports act as the exciter is shown. In the chandelierconfiguration, each of the antenna elements is set equidistance fromeach other and is set in a square like shape. The 4-port eNode maintainsphase synchronization between transmit and receive points using a localoscillator or via an external reference. The view is from overhead andshows eNode setup such that 3 of 4 ports receive (21-1, 21-2 and 21-3 inFIG. 13A) and 1 transmits (21-5 in FIG. 13A). The tag of interest isdenoted with a square (21-4). In each figure, three ellipses aredisplayed. Each ellipse shares a TX antenna for one focus and has adifferent receiving antenna as another focus. The intersection of allthree ellipses can be taken as an observation of tag location. Insituations where this intersection is not unique (not shown here)information regarding which antenna excited the tag can be used toidentify the most likely 3-way intersection. In one embodiment, thephase of a received tag signal is determined via correlation with apreamble sequence. Such an approach is described in U.S. patentapplication Ser. No. 11/770,712, filed Jun. 28, 2007, entitled “RFIDBeam Forming System”, the disclosure of which is hereby incorporated byreference as if set forth in full herein.

The confidence with which observations of location estimation can bemade is dependent upon the noise in the system. When an observation ismade at the intersection of ellipses in the manner outlined above, theconfidence of the observation can be gauged by the extent to which theellipses are approximately parallel at the point at which they intersect(see for example FIG. 3D). When the ellipses are approximately parallel,small variations in phase noise can result in significant shifts in theobserved RFID tag location. As is discussed below, the number andlocation of the antennas of a multiport exciter can significantlyincrease the confidence of location observations made using the exciter.

In FIG. 14A-D, the 4-port eNode is configured as an “offset lineararray”. In an offset linear array configuration, pairs of antennaelements are linearly aligned and equally distanced from each other.Also, the first pair is offset a set distance from the second pair. Theoffset configuration is used to increase total percent area where thebackscattered signals from an RFID tag will result in a locationobservation in which there is only one possible location of the RFID. Asshown, three ellipses are displayed for each of the four ports of theeNode with the intersection of the ellipses identifying a tag location.As is discussed further below, each of the four sets of three ellipseobservations can be passed to a particle filter process with any oneresult being sufficient to identify the location of the tag.

FIGS. 15A-F provide another example of a 4-port eNode configured as anoffset linear array exemplifying the potential coverage to locate one ormore RFID tags. For ease of the reader of the application, the array isshown with a single transmitter configuration with three ellipses drawnand the intersection of which identifying six different RFID taglocations.

FIG. 16 illustrates an example of locating an RFID tag using an“irregular” array. In such a configuration, the antenna elements are setup in a pseudo random pattern. In the illustrated example, six totalreceive patches (21-1, 21-2, 21-3, 21-6, 21-7 and 21-8) and one transmitpatch (21-5) are used to locate a RFID tag (21-4). The arbitraryconfiguration of the array can also be used for group delay basedlocation solving.

Observables Used in Location Estimation

Backscattered signals from RFID tags provide a variety of observablesthat can be used in location estimation. The observable used as a proxyfor distance in the above discussion of transmit and receive antennageometries is the calibrated slope of the group delay. Group delaydescribes the differences in phase observed at different frequencies.The manner in which group delay can be used in location estimation inaccordance with embodiments of the invention is explained below. Inseveral embodiments, observations of read rate are used in locationestimation. An RFID tag's read rate can be generally described as thenumber of times the RFID tag is read as a ratio of the number ofopportunities in which the RFID tag could have been read. Otherobservables that can be utilized in location estimation include, but arenot limited to, phase, phase coefficient magnitude, read rate, carrierfrequency, excitation node index, and receive antenna index.

Group Delay as an Observable

In FIG. 18, a flowchart describing one aspect of a location estimationprocedure is provided. The process commences when the reader transmits(8-1) an activation signal to a predetermined exciter. The exciterilluminates (sends interrogation signals) a RFID tag in the exciter'stransmit field (8-2). The RFID tag responds by sending informationsignals (8-3) in which the reader determines a group delay of thesignals (8-4). The RFID receiver system causes the RFID tag to respondin which the information signals differ in frequency only and the systemdetermines the phase difference between the different informationsignals. Using the ratio of the difference in phase versus thedifference in frequency (i.e. the group delay), the reader determinesthe distance to the RFID tag. In one embodiment, the reader alsodetermines total round trip time (8-5). As is discussed below, thedistance to the RFID tag can be determined using the group delay (8-6).It should be noted that throughout this process the signals communicatedto each system is frequency and phase locked.

Assuming that the exciter location from the reader is known, thereceived phase of the tag signal at the reader is measured. If adifferent tone frequency is used, a different relative phase will bemeasured. The difference in measured relative phases of the two tones attwo different frequencies due to the round trip delay is related to thedifferential frequency via (assuming the exciter is co-located with thereader):

$\begin{matrix}{{\Delta \; \varphi} = {2\; \frac{2{\pi\Delta}\; {fd}}{\; c}}} & (6)\end{matrix}$

where Δφ is differential relative phases, Δf is differential frequency,d is distance, and c is the speed of light. The phase θ₁ at tonefrequency f₁ can be measured with a 2 mπ ambiguity. Similarly the phaseat tone frequency f₂ can be measured with a 2nπ ambiguity. As long asthe differential phase is less than 2π, the phase difference of themodulo 2π measurements can be used to determine the range d given Δf.This is true as long as Δφ is less than 2π. Note that the condition canbe satisfied by selecting the appropriate frequency separation given theexpected range of operation. From the range d and bearing θ, the taglocation can be determined for the two-dimensional example. One skilledin the art would appreciate the extension to 3D is achievable andcontemplated. When the exciter is not co-located with the reader and hasdistance d₁ to the tag, then

${\Delta \; \varphi} = \frac{2\pi \; \Delta \; {f\left( {d + d_{1}} \right)}}{c}$

Angle of Arrival (AOA) as an Observable

In systems that include the specialized case of linear antenna arrays,observations of angle of arrival (AOA) from multiple linear arrays canbe used to triangulate an RFID tag. In addition, multiple observationsmade from a single linear array at different frequencies can be used totriangulate an RFID tag.

One example of a technique for observing location using AOA is based ona set of techniques known as Multiple Signal Classification (MUSIC)algorithms with spatial smoothing. In particular, to simplify notations,we examine the technique as applied to a four element linear array usingthe MUSIC algorithm with forward and backward filtering. One skilled inthe art would appreciate the extension of the algorithm to an arbitraryarray is achievable and contemplated.

The signals r_(i)(t) received by the ith element of an M-element lineararray each separated by a fixed distance, say λ/2, are given by

$\begin{matrix}{{r_{i}(t)} = {{\sum\limits_{k = 1}^{N}{a_{k}{s_{k}(t)}^{{- {j{({i - 1})}}}\pi \; {si}\; n\; \theta_{k\;}}}} + {n(t)}}} & (4)\end{matrix}$

where a_(k) is the amplitude of the k-th multipath signal. s₁(t) is thedesired signal, s_(k)(t) for k=2, 3, . . . , N are multipath receivedsignals, θ_(k) is the angle of AOA relative to the antenna boresight forthe k-th signal, and n(t) is additive noise or interference.

The inphase and quadrature components, namely I_(n), Q_(n) denote thereal and imaginary part of the received signal r_(i)(t). In vectornotation:

$\begin{matrix}{{{r(t)} = {{{As}(t)} + {n(t)}}}{{{s(t)} = \begin{bmatrix}{s_{1}(t)} \\{s_{2}(t)} \\\vdots \\{s_{N}(t)}\end{bmatrix}};{A = \left\lbrack {{a\left( \theta_{1} \right)},{a\left( \theta_{2} \right)},\ldots \mspace{14mu},{a\left( \theta_{N} \right)}} \right\rbrack};}{{a\left( \theta_{i} \right)} = \begin{bmatrix}a_{i} \\{a_{i}^{{- {j\pi}}\; s\; i\; n\; \theta_{i}}} \\\vdots \\{a_{i}^{{- {j{({M - 1})}}}\pi \; {si}\; n\; \theta_{i}}}\end{bmatrix}}} & (5)\end{matrix}$

where θ is the AOA relative to the antenna boresight. The signals s(t)includes the desired signal and (N−1) multipath signals.

Referring now to FIG. 21, the location determination of an RFID tag 10-1through multiple AOA measurements is illustrated. In thistwo-dimensional rendering, the tag location can be determined from twoAOA measurements when the locations of the two array antennas 10-2 and10-3 are known. In particular, the location (x, y) of the tag can bedetermined from:

$\begin{bmatrix}x \\y\end{bmatrix} = {\begin{bmatrix}1 & {- {\tan \left( \theta_{1} \right)}} \\1 & {- {\tan \left( \theta_{2} \right)}}\end{bmatrix}^{- 1}\begin{bmatrix}0 \\{x_{2} - {{\tan \left( \theta_{2} \right)}y_{2}}}\end{bmatrix}}$

Referring now to FIG. 20, one aspect of an array antenna RFID systemoperating in a near field mode in terms of its ability to locate RFIDtags is shown. A simplified four-element array is shown in a 2Dillustration of an example of the RFID tag location provided by thesystem. One skilled in the art would appreciate the extension of the 2Dto an arbitrary array in 3D is achievable and contemplated. The RFID taglocation technique is based on measuring a phase difference 9-1 of thearrival signals between a particular element 9-3 and a reference element9-2 or a preamble signal. The phase difference 9-1 is proportional tothe range difference (r₂ ^(o)−r₁ ^(o)) of the paths 9-4 and 9-5 betweenthe RFID tag and the two array elements 9-2 and 9-3. In particular, thedifferential range is given by

$\begin{matrix}{{r_{2}^{o} - r_{1}^{o}} = {\frac{c}{2\pi \; f}\Delta \; \theta_{1}}} & (2)\end{matrix}$

where f is the carrier frequency of the RFID tag. The location of theRFID tag, uniquely determinable from x₁, x₂, x₃, x₄ and differentialrange (r₂−r₁), (r₃−r₁), (r₄−r₁) can be calculated from the knownlocations of the array elements (x_(i), y_(i)) and the measureddifferential ranges from the very efficient algorithm where

$\begin{matrix}{{{G = \begin{bmatrix}{x_{2} - x_{1}} & {r_{2} - r_{1}} \\{x_{3} - x_{1}} & {r_{3} - r_{1}} \\{x_{4} - x_{1}} & {r_{4} - r_{1}}\end{bmatrix}};{Q = \begin{bmatrix}1 & 0.5 & 0.5 \\0.5 & 1 & 0.5 \\0.5 & 0.5 & 1\end{bmatrix}};}{{h = {\frac{1}{2}\begin{bmatrix}{\left( {r_{2} - r_{1}} \right)^{2} - x_{2}^{2} + x_{1}^{2}} \\{\left( {r_{3} - r_{1}} \right)^{2} - x_{3}^{2} + x_{1}^{2}} \\{\left( {r_{4} - r_{1}} \right)^{2} - x_{4}^{2} + x_{1}^{2}}\end{bmatrix}}};{r_{i}^{2} = {{\left( {x_{i} - x} \right)^{2} + {y^{2}\begin{pmatrix}\hat{x} \\\hat{y}\end{pmatrix}}} \approx {\left( {G^{T}Q^{- 1}G} \right)^{- 1}G^{T}Q^{- 1}h}}}}} & (3)\end{matrix}$

without loss of generality we assumed y_(i)=0.

The solution is based on weighted linear Least Square (LS) solution tofinding the intersection of hyperbolic curves defining the differentialranges. The accuracy of the solution approaches that predicted by theCramer-Rao Bound (CRB).

Read Rate as an Observable

Read rate is the ratio of the number of times an RFID tag is read to thenumber of times in which the RFID tag could have been read duringexcitation of an exciter. Systems that utilize distributed exciterarchitectures can have receive sensitivity so high that the main factorinfluence tag read rate is path loss between transmitter and tag.Therefore, read rates are expected to be correlated to the location ofthe tag with respect to the exciter. For example, if hypothesis regionx_(a) is located at equal distance from exciters e₁ and e₂, then itsrespective read rates RR_(e) ₁ and RR_(e) ₂ for an RFID tag locatedwithin the hypothesis region are expected to be equal. In determiningread rates, collisions can impact data. Typically, a balance is struckbetween avoiding collisions and ensuring that the number of slotsprovided to avoid collisions is not so great as to materially impact theperformance of the system.

An excitation link margin is used to generate a probability massfunction (pmf) that describes the likelihood that a tag will be read agiven percentage of the time (Read Rate) if it is located withininventory region x_(a). Read Rate is determined time interval anddividing this quantity by the number of total possible reads that werepossible in the same time duration. Read Rates (RR) will be indexed byexciter (e^(j)) using notation RR_(e). Given the preceding definitionsit is possible to specify the probabilities as a point on a Gaussianprobability mass function:

${p\left( y^{e} \middle| x_{a} \right)} = {\frac{1}{\sqrt{2\pi \; \sigma^{2}}}^{\frac{- {({{RR}_{e} - \mu})}^{2}}{2\sigma^{2}}}}$

Where μ and σ are determined as a function of excitation power, anglefrom exciter to hypothesis region, distance from exciter to hypothesisregion, exciter radiation pattern, and tag radiation pattern. Note thatprior to determining the probability of reading an RFID tag at a givenlocation, all probabilities associated with a given exciter, e, arenormalized such that:

${{\sum\limits_{a \in H}{p\left( y^{e} \middle| x_{a} \right)}} = 1}H \equiv {{The}\mspace{14mu} {set}\mspace{14mu} {of}\mspace{14mu} {all}\mspace{14mu} {hypothesis}\mspace{14mu} {regions}}$

As is discussed further below, a variety of estimators can be utilizedto determine the location of the hypothesis regions and obtain locationestimates for RFID tags observable within the various hypothesisregions.

Estimation Location Using Observables

Referring now to FIG. 19, the operation provided in FIG. 18 is repeated(8-7). The repeated operation produces multiple read points. Utilizingthese multiple reads and thus the information or distance estimations ofthe RFID tag, a probability distribution model is formed (8-8). Analgorithm can be selected and applied (8-9) to determine a locationestimate of the RFID tag. Additionally, a confidence level or accuracyfactor can be determined. As a result of applying the algorithm themultipath effects, e.g., the direction of arrival of the signal from thesource to each antenna element are accounted for and mitigated. Variousestimators are discussed below.

Estimators Used to Obtain Location from Observables

The impact of noise present in observations of RFID tag location can belimited using estimators. A number of different estimators that can beused to estimate RFID tag location using any of the observables outlinedabove are discussed below.

Particle Filter Based Estimators

In one embodiment shown in FIG. 18 the phase, phase coefficientmagnitude, read rate, carrier frequency, excitation node index, receivepatch antenna index phase, and/or other observables associated with agiven tag read (as described in the preceding text on observables) ispassed to a Monte Carlo hypothesis testing algorithm known as a particlefilter (25-4). Since a generalized 3D probability distribution functionis continuous and hence technically infinite in complexity, a finitecompressed description of this distribution is found. Kalman filtersprovide only a second-moment description of this general distribution.Unscented filters are good to 3rd moment complexity. It is also possibleto consider gridded hypotheses, where a set of hypotheses are kept fromacross the domain of the state, and can be extended to higher pointdensity. However, these can be very wasteful in the number of hypothesesneeded to express a localized likelihood.

Particle filters are an adaptive hypothesis approach to estimation thatuses a non-uniform time adaptive grid. Particles, which represent testhypotheses in state space, are generated based on the prior distributionof the state. For each observation, the likelihood that a given particle(the state possibility) generates that observation is assessed. Highlylikely particles are replicated, unlikely particles are eliminated.Finally, replicated particles are randomly moved a small amount in statespace, similar to a genetic mutation or annealing.

For our location estimation problem particles are associated with a(x,y,z) location and optionally a ({dot over (x)},{dot over (y)},ż)velocity. Particle filtering can be divided into an initializationprocess and a recurring set of processes that operate on each newmeasurement. We use the first measurement distribution, or other priorstats, to generate the seed particle cloud. If the state were a uniformdistribution over a finite extent, these initial particles could bechosen from a grid. But in general, the prior distribution is morecomplicated, and random state values are chosen to seed the particleset.

The time update process (25-5) is performed whenever a new observation(25-3) enters the system. It corresponds to the propagation of theparticle states and dynamic uncertainty due to the fact that some timehas passed since the last update. This step is driven by physicalprocesses, deterministic and stochastic. Given that some amount of timethat has passed since the last update there is some uncertainty aboutthe current position and velocity of each particle. We relocate eachparticle to a random new position and a random new velocity. Thedistribution used for this processes depends on the environment and thecurrent state of the particle. As an example, if we wish to estimate thelocation of tags moving on a forklifts the new velocity is limited tothe velocities that can be obtained by an acceleration of 1G or less ineach direction. There's also a maximum absolute value of velocity thatthe forklift can have. The time update process is separate from theregularization step in principle, but is dependent in implementation,since both time update and regularization add noise to state particles

Next the measurement update (25-6) process computes likelihoodsassociated to each particle given the new measurement. The resultinglikelihood is the product of the likelihoods that each observation (forinstance phasor, or read rate measure) correspond to given the expectedphase between eNode to tag and tag to antenna element distances. Theseprobabilities can be evaluated with, in one embodiment, a Gaussiandistribution that uses a standard deviation which depends on the receivepower on the antenna when the observation is taken and also on thereliability associated with the estimated calibration coefficient.Calibration coefficients are used on each tag-read measurement in orderto remove any effects that don't correspond to wave propagation. In oneembodiment, given that the distance between an excitation point and areceive patch in known, one can remove excess phase rotation at eachfrequency compared to observed phase using a ‘backchannel’ waveform orreference tag that is co-located with the excitation point (25-10). Theamount of removed excess rotation at each frequency is recorded and‘backed-out’ of subsequent received tag measured phase data in order tocompensate for phase rotation effects not due to radio propagation (suchas electronic delay).

The re-sampling process (25-7) is in charge of destroying/cloningparticles based on their likelihood. This is done by taking thecumulative distribution generated by the particles likelihoods and usingit to generate the new particles. The more likely a particle is, themore it will be cloned. Clones of particles have the same position andvelocity (in other words, they are exact clones for now; the next step(regularization) adds carefully chosen mutations)

The final step in the particle filter process, regularization (25-8), isresponsible for keeping some memory of measurement likelihoods. Previousprobabilities of particles are captured by reproduction and mutation. Inthis way particles with high are replicated. The regularization processis similar to genetic mutation or simulated annealing. Its purpose is tojitter clones to fill in gaps in the particle set. One of the knownproblems of particle filters is the possibility that the points collapseto a small number of hypothesis. If the cloud of particles collapses,there will be too few hypotheses to test in future measurements. Theregularization process, through its introduction of random variation, isin charge of avoiding this problem.

Results are finally output for higher layer static gathering in (25-9).At this layer it is possible to compute probability densities for thelocation solution over time. In general one can report the quality ofthe final solution via variance measurement of this final layerstatistic.

Bayesian Estimators

In one embodiment, signals for a selected RFID tag from whichinformation is to be derived may be selected from signals from aplurality of RFID tags based on a spatial location of the selected RFIDtag relative to the spatial location of other ones of the plurality ofRFID tags. That is for a given interrogation space, only a specificpopulation of tags are illuminated as depicted in FIG. 3 and as anexample the interrogation space 3-8. For estimating the location of thetag, a Bayesian approach is used to model the probability densityfunction of the location of an RFID tag, based on all availableinformation such as the AOA, position of excitation node and anyprevious knowledge of the position of the population of the sensor(s) ortag(s), the multi path propagation environment, the frequency of themeasurement, and the array response (beam pattern) where the measurementand any other auxiliary information to further inject in the aprioriBayesian model. In one aspect, the system recursively estimates thelocation in the three dimensional Euclidean space (position in x, y, z,that is elevation, roll, yaw).

The observed vector Y_(t) ^(j) (for the j-th tag) measured at eachantenna element comprises of discrete complex valued received signalsamples r_(t) or equivalently the in-phase and quadrature componentsI_(n), Q_(n) for each antenna element with real and complex partrespectively, known position of exciter (x,y,z), beam formercoefficients a, Signal-to-Noise Ratio (SNR) Estimate, gain setting α,soft metric, extrinsic information β(I_(n),Q_(n)), and packets (e.g.RN16+EPC code) for each interrogation space. The model measurement usedis a single vector at time t, Y_(t) ^(j). It is assumed that theobserved L-dimensional vector Y_(t) ^(j) results from mapping the3-dimensional Euclidean space of the location of the tag to anL-dimensional observable vector R³→R^(L). Different approaches ofestimating the probability distribution P(x_(t) ^(j)|Y_(t) ^(j))recursively are provided in which x_(t) ^(j) is the location coordinatesof the j-th tag in 3 dimensions. The conditional expectation (i.e. meanvalue E(x|Y)) of this density represents that the location of the tag orequivalently is isomorphic to the estimation of this sequence.

Referring now to FIG. 22, for entire space or volume 12-1, an exciteractivated by the reader produces a RFID location estimate thatgraphically is shown in 12-4. As multiple iterations of reads occur, theRFID location estimates for multiple RFIDs are graphically shown in12-5. The peaks or centroid of the cones identifies the location of anRFID. The surrounding portion of the cone identifies the accuracy orcertainty of the identified location. For example, the peak 12-5 a beingstepper provides an highly accurate location of an RFID tag while thepeak 12-5 b being flatter provides a less accurate estimate location ofthe RFID tag.

With a priori knowledge of the location of the transmitter/exciter(11-2), the problem of estimating a tag's location can be reduced tofinding the location of tag in the cube shown in 12-1. Utilizinghypothetical testing, the cube can be further quantized for location ofeach tag to smaller cubes as shown in 12-2, with each location treatedas sphere 12-3. The probability distribution of the location ofpopulation of the tags in the interrogation space can be viewed as a twodimensional Gaussian density with a known mean and variance in twodimensional (x,y) Euclidean space. The spheres projected into circlescan also respectively be viewed as a two dimensional Gaussian densitywith a known mean and variance in two dimensional (x,y) Euclidean spaceas illustrated by graph 12-5. In case of three dimensional sphere 12-3in a three dimensional Euclidean space, the (x,y,z) dimension of eachpoint becomes the support of a three dimensional Gaussian density. Inthis manner, for certain class of algorithms described later, thealgorithm can be initialized with a known a priori probability densitymodels as illustrated by graph 12-5.

Referring now to FIGS. 23-25, exemplary procedures and formulations tofurther refine and/or provide additional accuracy to the locationestimation of an RFID tag are provided. In the following, the observedsample space Ω={Y_(t) ^(j),∀t,j} is denoted. P(x_(t) ^(j)|Y_(t) ^(j))represents the probability distribution function location at time t,based on all the past AOA measurements. Bayes models provide aprobabilistic framework for the recursive estimation of P(x_(t) ^(j)|θ),where the vector θ=(θ_(j) . . . θ_(t)) defines the angle of arrivalvector.

In an indoor propagation environment, the direction of the dominantsignal may be due to a reflected signal instead of the direct path insome cases. This situation is accounted for to avoid an erroneousestimate of the actual location of the source. The location of a tag{x_(t);tεN},x_(t)εX (t may also represent an iteration index) is modeledas a 1^(st) order Markov process with initial distribution p(x₀) and theMarkov relation P(x_(t)|x_(t−1)). The observed sequence of tag signalsY_(t)εΩ may include both complex and real-valued measurements andestimates made by the reader system for each array element are providedin FIG. 23.

The observed vector from the j-th tag is denoted by Y_(t) ^(j)=(y_(t)^(j), y_(t−1) ^(j), y_(t−2) ^(j), . . . y₀ ^(j)) with each y_(t) ^(j) isa vector. The P(x_(t) ^(j)/Y_(t) ^(j)) probability density function inone aspect is obtained recursively in two stages, namely prediction andupdate stages. The a priori probability density function at time step tused to predict x_(t) (for clarity the dependency on j is removed) is

P(x _(t) |Y _(t−1))=∫P(x _(t) |x _(t−1))P(x _(t−1) |Y _(t−1))dx_(t−1)  (7)

and the update via Bayes rule is

$\begin{matrix}{{P\left( x_{t} \middle| Y_{t} \right)} = \frac{{P\left( y_{t} \middle| x_{t} \right)}{P\left( x_{t} \middle| Y_{t - 1} \right)}}{P\left( y_{t} \middle| Y_{t - 1} \right)}} & (8)\end{matrix}$

where, P(y_(t)|y_(t−1))=∫P(y_(t)|x_(t))P(x_(t)|y_(t−1))dx_(t) withinitial condition P(x₀|Y₀)Equation (8) can be viewed as P(x_(t)|Y_(t))=W_(t)P(x_(t)|Y_(t−1)) wherethe weight is defined by

$\begin{matrix}{W_{t} = \frac{P\left( {y_{t}x_{t}} \right)}{P\left( {y_{t}Y_{t - 1}} \right)}} & (9)\end{matrix}$

In various aspects, multiple approaches for recursive estimation ofP(x_(t) ^(j)|Y_(t) ^(j)) are provided. FIG. 24 outlines the steps of thegeneral form of sequential Monte Carlo methods. The conditional densityfunction P(x_(t+1) ^(j)|Y_(t) ^(j)) is updated in each iteration 14-1,the weights are updated based on a function (to be defined later) ofprevious values of the weights and possibly a random parameter p (to bedefined shortly) in 14-2. The prediction step 14-3 involves computingP(x_(t+1) ^(j)|Y_(t) ^(j)) and then obtaining a new sample Y_(t+1) ^(j)from the sample space. This last step is referred to as resampling.

One resampling approach is to evaluate the density with a pointwiseapproximation. Using a classical Monte Carlo method, the empiricaldistribution of x_(t) is given by an application of histogram averagingvia

${\hat{P}\left( x_{t} \right)} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}{\delta \left( {x_{t} - {x(i)}} \right)}}}$

where {x(i)} is drawn from a random source with a probabilitydistribution P(x). Each time a set of measurements is made, thelikelihood of each prior measurement can be estimated.

In accordance with various aspects, the system initializes multiplesolutions as described earlier. FIG. 25 provides a general approach inaccordance with various aspect of finding the location of an RFIDtag/sensor. Beginning with multiple candidate solutions and eachsolution set itself are applied to one of the many choices forestimating the likelihood as shown in 15-2, by sampling Ω in 15-1, andcomputing the probability density P(x_(t+1) ^(j)|Y_(t) ^(j)). Varioustechniques outlined in 15-3 such as rejection sampling, importancesampling and sampling importance resampling (SIR), simulated annealing,particle filtering and unscented transform approaches can be used. Eachof these techniques utilize a slightly different approach to computingthe weight sequence in resampling over time, where resampling isperformed with replacement N-times from Ω in all the cases.

A Sampling Importance Resampling Estimator

A recursive SIR approach is performed as follows:

-   -   1. Set t=0 and get M samples x₀ ^(i)εΩ for i=1, . . . , M    -   2. Weight Update: Compute the likelihood weights        w_(i)=P(y_(t)|x_(t) ^(i)) for i=1, . . . , M    -   3. Normalize the weights by

$\partial{= {\sum\limits_{j = 1}^{M}w_{j}}}$ with$w_{i}^{*} = \frac{w_{i}}{\partial}$

-   -   4. Resampling: Generate a new set {tilde over (x)}_(t) ^(i)εΩ        with i=1, . . . , N with replacement N times from the discrete        set {x_(t) ^(j), j=1, . . . , M} with P({tilde over (x)}_(t)        ^(i)=x_(t) ^(j))=w*_(j)    -   5. Prediction: predict each of the resampled states        independently k-time, where x_(i+1) ^((i−1)k+m)∝P(x_(t+1)|{tilde        over (x)}_(t) ^(i)) with i=1, . . . , N and m=1, . . . , k    -   6. go to step 2 and repeat t=t+1

To avoid a degenerate solution where after some iterations only onecandidate state vector value is present, the resampling step maymodified by using a known distribution around the expected location ofRFID tags near the exciter. The choice of importance function in eachcoordinate will be an independent identically distributed Gaussiandistributed density N(m, σ) with the mean m and variance σ² of thedensity to be equal to the location of the exciter plus a correctionterm (mid-range between exciter and the farthest tag illuminated by theexciter) and variance σ² to be equal to one of the diameters of theellipsoid in the three dimensional Euclidean space.

In this case the importance sampling is achieved by generating samplesfrom a proposed distribution q(X_(t)|Y_(t))=q(x_(t)|x_(t−1) ^(i),Y_(t))q(X_(t−1)|Y_(t−1)). In this version to determine the degeneracy ofthe particle cloud, the relative efficiency of the importance samplingprocedure is related by the ratio between the variance of the importancesampling estimate and the variance of the estimate if a perfect MonteCarlo simulation was possible. The quantity can be estimated by

${\overset{\Cap}{N}}_{eff} = \frac{1}{\sum\limits_{i}^{N}w_{i}^{2}}$

and N_(thresh) is a preselected threshold where the resampling procedureis applied to the set of the particles.

An Enhanced Particle Filter Estimator

In one embodiment, an enhanced particle filtering approach begins with ageneration or selection of N inputs or samples (Set t=0 and

$w_{0}^{i} = \frac{1}{N}$

get N samples x₀ ^(i)εΩ for i=1, . . . , N from q(x₀|y₀)). Weights foreach sample (i=1, . . . , N) are then computed in accordance with thefollowing function:

$w_{t}^{i} = {w_{t - 1}^{i}\frac{p\left( {y_{t}x_{t - 1}^{i}} \right)}{q\left( {{x_{t}x_{t - 1}^{i}},Y_{t}} \right)}}$

and normalized:

$\partial{= {\sum\limits_{j = 1}^{M}w_{j}}}$ with$w_{i} = \frac{w_{i}}{\partial}$

If the relative efficiency is greater than a preselected threshold({circumflex over (N)}_(eff)>N_(thresh)) then resampling is skipped.Otherwise, resampling is performed by generating a new set {tilde over(x)}_(t) ^(i)εΩ with i=1, . . . , N with replacement Ntimes from thediscrete set {x_(t) ^(j)=1, . . . , M} with P({tilde over (x)}_(t)^(i)=x_(t) ^(j))=w_(j) and weights are reset

$w_{0}^{i} = {\frac{1}{N}.}$

Prediction is then performed for each of the states or resampled statesindependently of k-time, where x_(i+1) ^(i)∝q(x_(t+1)|x_(t) ^(i),Y_(t+1)) with i=1, . . . , N and m=1, . . . , k. The process is thenrepeated for the next set (t=t+1) and the computation of the weights forthe new samples.

A Metropolis-Hastings Algorithm Estimator

Using a Markov chain model for the observed sequence and estimation whena proposed distribution is used to generate the samples, theMetropolis-Hastings algorithm, a candidate sample z is drawn from theproposal q(z|x) and accepted with a probability given by (p, q, πrepresenting different distributions)

${\alpha \left( {x,z} \right)} = {\min \left( {1,\frac{{\pi (z)}{q\left( {xz} \right)}}{{\pi (x)}{q\left( {zx} \right)}}} \right)}$

The candidate is accepted or rejected, as the Markov chain moves to thenew data set, while the rejection leaves the Markov chain at the currentdata point in the state space. If π(x)=p(x|y) is chosen, then theacceptance probability is simply:

${\alpha \left( {x,z} \right)} = {\min \left( {1,\frac{p\left( {yz} \right)}{q\left( {yx} \right)}} \right)}$

Metropolis-Hastings algorithm is summarized as follows:

-   -   1. Set t=0 and choose x₀ randomly or deterministically    -   2. Sample z˜q(z|x_(t)) and u˜U(0,1)    -   3. Compute acceptance probability: α(x, z)    -   4. Predict: If u≦α(x, z) accept the new sample x_(t+1)=z        otherwise x_(t+1)=x_(t)    -   5. Go to step 2 and repeat t=t+1        In step 4, by adopting a statistical mechanics approach with        introducing an energy or fitness function for the state of the        system, then the probability density in phase space of the point        representing x_(t) is proportional to e^(−βE(x) ^(t) ⁾ where

${\beta = \frac{1}{kT}},$

T is the absolute temperature in Kelvin and k is the Boltzman constant1.38×10⁻²³ J/Kelvin. The energy or fitness improvement withtransitioning from one state to another can be characterized as thedifference between the two energy state, i.e. ΔE=E(x_(t+1))−E(x_(t))such that the energy is reduced in each iteration, that is transitionprobabilities of the state is:

$x_{t + 1} = \left\{ \begin{matrix}{\overset{\sim}{x}}_{t\;} & {{with}\mspace{14mu} {probability}\mspace{14mu} e^{{- {\beta\Delta}}\; E}} \\x_{t} & {{{with}\mspace{14mu} {probability}\mspace{14mu} 1} - e^{{- {\beta\Delta}}\; E}}\end{matrix} \right.$

where {tilde over (x)}_(t)εΩ. If an additional constraint is applied toreduce the temperature T monotonically such that T_(n)<T_(n-1)<T_(n-2)in each iteration for a set of the data with the initial condition ofT₀>>T_(n)<T_(n-1)<T_(n-2), it is expected for the solution to convergeto the near optimal estimate, by utilizing the trajectory of thesolution phase space with the property of following the states of anaperiodic and irreducible Markov chain.

An Unscented Transform Estimator

Unscented transform is another approach to estimation of the locationfor the RFID tags. By defining the covariance matrixP=E((x−x)(x−x)^(T)), where x denotes the mean value of the randomvariable x, the problem of approximating the distribution of anN-dimensional random variable with a mean and covariance is approached.The affine transform x=x+(√{square root over (X)})z is defined where√{square root over (X)} is the matrix square root of X with property√{square root over (X)}√{square root over (X)}^(T)=X. The unscentedtransform approach is thus summarized as follows:

-   -   1. Initialize

${\overset{\_}{x}}_{0} = {{{E\left\lbrack x_{0} \right\rbrack}\mspace{14mu} P_{0}} = {E\left\lbrack {\left( {x_{0} - {\overset{\_}{x}}_{0}} \right)\left( {x_{0} - {\overset{\_}{x}}_{0}} \right)^{T}} \right\rbrack}}$${\overset{\_}{x}}_{0}^{a} = {{E\left\lbrack x^{a} \right\rbrack} = \begin{bmatrix}{\overset{\_}{x}}_{0}^{T} & 0 & 0\end{bmatrix}^{T}}$$P_{0}^{a} = {{E\left\lbrack {\left( {x_{0}^{a} - {\overset{\_}{x}}_{0}^{a}} \right)\left( {x_{0}^{a} - {\overset{\_}{x}}_{0}^{a}} \right)^{T}} \right\rbrack} = \begin{bmatrix}P_{0} & 0 & 0 \\0 & Q & 0 \\0 & 0 & R\end{bmatrix}}$

-   -   2. Define

χ_(t−1) ^(a) [x _(t−1) ^(a) x _(t−1) ^(a)±√{square root over (n _(a)+λ)P_(t−1) ^(a))}]

-   -   3. Time update

χ_(tt − 1)^(x) = f(χ_(t − 1)^(x), χ_(t − 1)^(v))${\overset{\_}{x}}_{t{t - 1}} = {\sum\limits_{i = 0}^{2n_{a}}{W_{i}^{(m)}\chi_{i,{t{t - 1}}}^{x}}}$$P_{t{t - 1}} = {\sum\limits_{i = 0}^{2n_{a}}{{W_{i}^{(c)}\left\lbrack {\chi_{i,{t{t - 1}}}^{x} - {\overset{\_}{x}}_{t{t - 1}}} \right\rbrack}\left\lbrack {\chi_{i,{t{t - 1}}}^{x} - {\overset{\_}{x}}_{t{t - 1}}} \right\rbrack}^{T}}$Y_(tt − 1) = h(χ_(t|t − 1)^(x), χ_(t − 1)^(n))${\overset{\_}{y}}_{t{t - 1}} = {\sum\limits_{i = 0}^{2n_{a}}{W_{i}^{(m)}Y_{i,{t{t - 1}}}}}$

-   -   4. Weight update

W _(t) ^(c) =W ^(c) _(t−1) P(y _(t) ,x _(t))

λ is a composite scaling parameter, n_(a)=n_(x)+n_(v)+n_(n), Q isprocess noise covariance. R is measurement noise covariance matrix.

A Differential Evolution Based Estimator

Another layer of optimization for finding the location of the RFID tagis to start from a population (instead of a single solution) of possiblesolutions. The initial population is chosen judiciously to cover thespace of the exciter range as much as possible. In one aspect, a uniformprobability distribution for all random locations is initially utilized.In case a preliminary solution is available, the initial population isoften generated by adding normally distributed random deviations to thenominal solution x_(nominal). Differential evolution (DE) provides anapproach for generating trial parameter vectors. DE generates newparameter vectors by adding a weighted difference vector between twopopulation members to a third member. If the resulting vector yields alower objective function value than a predetermined population member,the newly generated vector will replace the vector with which it wascompared in the following generation. The comparison vector can but neednot be part of the generation process mentioned above. In addition thebest parameter vector x_(Best,G) is evaluated for every generation G inorder to keep track of the progress that is made during the minimizationprocess. Extracting distance and direction information from thepopulation to generate random deviations results into a convergingsolution. A trial vector is introduced for each generation v=x_(r) ₁_(,G)+μ(x_(r) ₂ _(,G)−x_(r) ₃ _(,G)) with r₁, r₂, r₃ randomly chosenbetween 1 and L, where L is the number of generations to follow and is afixed parameter throughout the evolution and μ controls the step size ofthe differential variation from one generation to the other. Thismethodology is summarized in FIG. 26, starting with a population offeasible solutions 16-1, then performing some fitness computation in16-2, population pruning and mutation in 16-3, checking a stopping rulein 16-3.

An Ant Colony Optimization Based Estimator

In various other embodiments, other nonlinear stochastic optimizationalgorithms are utilized by considering a population of solutions andupdating each solution's viability via some selected metric. A metricused frequently and referred to as ant colony optimization, referred toas pheromone is defined by

$p_{ij}^{k} = \left\{ \begin{matrix}\frac{\tau_{ij}^{\alpha}}{\sum_{l \in N_{t}^{k}}\tau_{il}^{\alpha}} & {{{if}\mspace{14mu} j} \in N_{i}^{k}} \\0 & {{{if}\mspace{14mu} j} \notin N_{i}^{k}}\end{matrix} \right.$

When in iteration t, consider N possible solutions (as opposed tofollowing one solution) and follow k solutions and compute theprobability of solution j by setting a solution metricτ_(ij)←τ_(ij)+Δτ^(k) with initial condition τ_(ij)←(1−p)τ_(ij), ∀(i,j)εA. N_(i) ^(k) denotes the number of solutions in the neighborhood ofk^(th) solution in the i^(th) iteration. In this manner, potentialdegeneracy problems are avoided at an algorithmic level by consideringmultiple solutions concurrently in the solution space. This can beapplied to any of the algorithms described above, by considering eachsolution as a single point in a planar graph and finding the best pathin the graph via the solution metric outlined here. This approach issimilar to so called “genetic programming” or “ant colony optimization”.

Stopping Rules

In the accordance with various aspects, the approaches provided arewithout any specific constraints on the form or type of stopping rule.For brevity here, a number of different stopping rules are provided asused in accordance various aspects of the present invention. Here adistance is computed d(x_(t), x_(t+1)), for example |x_(t)−x_(t+1)| andif d(x_(t),x^(t+1))<<ε the algorithm is stopped. A region of attractionis defined by A={x_(t), d(x_(t), x*)<<ε} where x* denotes the optimalsolution and ε being a small positive number. For algorithms utilizing adiscrete Markov chain approach such as the Metropolis-Hastings method,the fraction of the uncovered state space is minimized such that aregion of attraction is reached. In this approach a count is used forvisiting each state of Ω and then incrementing it each time the state isvisited. The stopping rule is such that

${\underset{i}{Min}C_{i}} > 1$

and in addition the distance criterion is met.

Location Estimation in Vertical Racked Shelving Applications

An end application of the present invention is described in FIG. 27. Thefigure details a series of adjacent vertical racked shelves used tostore inventory items. Such shelving commonly appears in warehouses tostore inventory. With present technology the inventory must be manuallycounted, often with the aid of barcode reading devices, by moving fromone section of the shelving to another and recording an item's serialidentifier along with shelf x,y,z location. In contrast the RFIDlocation system as described throughout this description in variousembodiments is able to provide continuous status regarding items storedon shelves without movement of gear or personnel from one area to thenext. This is achieved in one embodiment through regular staticplacement of radio frequency excitation points overhead in the areabetween aisles. Utilizing excitation power control and/or read rates foran RFID tag on a per exciter basis in conjunction with the particlelocation procedures items in applications such as vertical rackedstorage can be located. In particular, read rates for an RFID tag on aper exciter basis and per excitation power level provide z dimensioninformation for the location of an RFID tag. It should however beappreciated that there is a special case of vertical racked storage,floor level storage (also known as floor staging of goods), such thatthe vertical location of goods (z) is known a-priori to be a knownconstant. It should also be appreciated that a special case of verticalracked storage exists when items are simply stacked on top of oneanother. In this case no physical shelf separates items in the “z”dimension; rather item height is delineated by stacking order.

While the above description contains many specific embodiments of theinvention, these should not be construed as limitations on the scope ofthe invention, but rather as an example of one embodiment thereof.Accordingly, the scope of the invention should be determined not by theembodiments illustrated, but by the appended claims and theirequivalents.

1. A method of locating one or more radio frequency identification(RFID) tags comprising: illuminating at least one RFID tag by anexciter; receiving information signals from the illuminated at least oneRFID tag by a plurality of receive antennas; determining a phasederivate for the received information signals from the at least oneilluminated RFID tag received by each of the plurality of receiveantennas; and identifying a location of the at least one RFID tag basedon the determined phase derivates of the received information signals.2. The method of claim 1 further comprising transmitting an activationsignal from a reader to an exciter, the reader and exciter sharing acommon geometric characteristic relative to each other.
 3. The method ofclaim 1 wherein the shared common geometric characteristic is anellipsoid wherein the reader and the exciter are foci of the ellipsoid.4. The method of claim 1 wherein identifying the location of the atleast one RFID tag is based on a frequency derivate of the receivedinformation signals.
 5. The method of claim 3 wherein identifying thelocation of the at least one RFID tag is based on a ratio of the phasederivate versus the frequency derivate.
 6. The method of claim 1 whereinidentifying the location of the at least one RFID tag is based on a readrate derivate of the received information signals.
 7. A radio frequencyidentification (RFID) system for locating one or more RFID tags, thesystem comprising: at least one exciter having a plurality of antennasand configured to selectively transmit interrogation signals through atleast two of the plurality of antennas and to selectively receiveinformation signals from at least one RFID tag through one of theplurality of antennas different from the at least two of the pluralityof antennas; and a reader in communication with the at least one exciterand configured to activate the at least one exciter, the reader locatingthe at least one RFID tag based on a phase derivate of the receivedinformation signals.
 8. The system of claim 7 wherein the plurality ofantennas are arranged in a uniform pattern.
 9. The system of claim 7wherein the plurality of antennas are positioned equidistant from eachother.
 10. The system of claim 7 wherein at least two of the pluralityof antennas are positioned as foci for a substantially ellipsoid thatidentifies a locus of potential locations of the at least one RFID tag.11. The system of claim 7 wherein the plurality of antennas arepositioned in pairs with each pair being foci for a substantiallyellipsoid that identifies a locus of potential locations of the at leastone RFID tag and each pair positioned to minimize overlapping of eachsubstantially ellipsoid.
 12. The system of claim 7 wherein the pluralityof antennas are positioned around the at least one exciter, eachlocation of each of the plurality of antennas differing from each other.13. The system of claim 7 wherein the exciter uses a carrier frequencyto mix a reference signal with the received information signals.
 14. Thesystem of claim 13 wherein the reference signal is a pilot and apreamble signal.
 15. A radio frequency identification (RFID) system forlocating one or more RFID tags, the system comprising: at least one RFIDtag; an antenna array configured to illuminate the at least one RFIDtag; a transmitter coupled to the antenna array and configured toactivate the antenna array to repeatedly illuminate the at least oneRFID tag within a specific time frame and a specific space; and a readerin communication with the transmitter and configured to generate aprobability model based on information signals received from therepeated illumination of the at least one RFID tag and the readerapplies a particle filter on the generated probability model todetermine a location of the at least one RFID tag based on a result ofthe applied particle filter.
 16. The system of claim 15 wherein theparticle filter further comprises computing weights for each locationinformation of the at least one RFID tag based on each illumination. 17.The system of claim 16 wherein the particle filter further comprisesnormalizing the computed weights.
 18. The system of claim 15 wherein theparticle filter determines a likelihood of a RFID tag location based ona phase derivate of the received information signals.
 19. The system ofclaim 15 wherein the particle filter determines a likelihood of a RFIDtag location based on observed read rate of the received informationsignals.
 20. The system of claim 16 wherein the particle filterreplicates and randomizes likely location information and discardsunlikely location information of the at least one RFID tag.